Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). T…
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48 results for “regions of competence”
Improved dynamic classifier selection by refining regions of competence.
problem Limited performance of dynamic selection systems due to noisy regions.
method Integrates a filter and an adaptive distance to enhance regions of competence.
result Significant increase in recognition performance and decrease in computational cost.
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correc…
Paper evaluates competence measures for DRS systems.
problem Choosing the best measure to quantify competence in DRS systems is challenging.
method Reviewed and adapted eight competence measures for regression problems, compared them on 15 datasets, and evaluated three DRS systems.
result DRS systems outperform individual regressors and static systems, but competence measure choice depends on the problem.
The study proposes a framework to accept OOD data based on competence scores.
problem Silent failures in Domain Generalization where models reject OOD data without proper justification.
method A learning to reject framework using proxy incompetence scores to predict trustworthiness.
result Increasing incompetence scores are predictive of reduced accuracy, but not always favorable for accuracy/rejection trade-off.
A new framework selects best outlier detectors locally for improved ensemble performance.
problem Challenges in combining outlier detectors without ground truth.
method Locally Selective Combination in Parallel Outlier Ensembles (LSCP) framework.
result LSCP_AOM variant consistently outperforms other methods on real-world datasets.
PaRCE estimates model confidence for CNNs across various uncertainties.
problem Limited holistic approach to estimating perception model confidence in CNNs.
method Probabilistic and reconstruction-based competency estimation.
result PaRCE best distinguishes between various types of samples and regions.
Analyzing different prototype selection techniques for dynamic classifier and ensemble selectioncs.LG
Prototype selection improves DS techniques' accuracy and reduces computational cost.
problem Improving the performance of dynamic selection techniques.
method Prototype selection techniques that edit validation data to remove noise and redundant instances.
result Improves DS techniques' classification accuracy and reduces computational cost.
Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy …
Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materialscond-mat.mtrl-sci
Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials.
problem Predicting magnetic ground states, moments, and anisotropy in two-dimensional magnets.
method Introduce the symmetry-electronic fingerprint (SEF), a physically interpretable representation that encodes crystallographic symmetry operations, Wyckoff-site geometry, and site-resolved electronic structure.
result SEF-trained models accurately classify magnetic ordering and regress moments alongside anisotropy energies.
Yield curve forecasting is an important problem in finance. In this work we explore the use of Gaussian Processes in conjunction with a dynamic modeling strategy, much like the Kalman Filter, to model the yield curve. Gaussian Processes have been successfully applied to model functional data in a variety of application…
Develops a Bayesian model to predict business revenue and demand.
problem Estimating revenue and demand at business facilities.
method Variational Bayesian spatial interaction model (BSIM) with scalable inference.
result BSIM outperforms competing approaches in predicting pub revenue and demand.
Compressed Diffusioncs.LG
A new method for efficient diffusion geometry computation from data regions.
problem Heavy computational load in diffusion maps for modern data analysis.
method Compressed diffusion process between data regions using an adapted MGC kernel.
result Efficient pointwise diffusion map embedding from data regions.
DCSO dynamically selects top-performing base detectors for outlier ensembles.
problem Challenges in selecting and combining outlier scores from different detectors.
method DCSO dynamically selects top-performing base detectors based on local k-nearest neighbors.
result DCSO provides consistent performance improvement over static combination approaches.
This paper tightens bounds on linear regions in piecewise linear neural networks.
problem Understanding the expressiveness of piecewise linear neural networks.
method Analyzes the maximal number of linear regions in single and multi-layer PLNNs, and rectifier networks.
result Derives tighter bounds on the maximal number of linear regions in PLNNs and rectifier networks.
Map projections can reverse district compactness scores, affecting fairness evaluations.
problem District compactness scores can be reordered by map projections, impacting fairness evaluations.
method Mathematical proof and empirical demonstration of map projection effects on compactness scores.
result Map projections can reverse the order of compactness scores, altering fairness evaluations.
Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifier's competence, the manner in which the pool is generated could affect the choice of the best cla…
FreB protocol uses AI to infer hidden parameters with valid confidence regions.
problem Generating biased or overconfident conclusions from AI-generated posterior distributions.
method Frequentist-Bayes (FreB) protocol reshapes AI-generated posterior distributions into valid confidence regions.
result FreB provides valid confidence regions that consistently include true parameters with expected probability.
New algorithms adaptively compete against complex environments with local regularities.
problem Efficiently competing against complex, locally regular comparator functions in nonparametric settings.
method Locally-adaptive online algorithms using hierarchical -nets and tree experts.
result Proved regret bounds scaling with different types of local regularities, delivering better performance for simple profiles.
We study the isoperimetric structure of asymptotically flat Riemannian 3-manifolds (M,g) that are C^0-asymptotic to Schwarzschild of mass m>0. Refining an argument due to H. Bray we obtain an effective volume comparison theorem in Schwarzschild. We use it to show that isoperimetric regions exist in (M, g) for all suffi…
SPLICE method disentangles shared and private latent variables from multi-view data.
problem Lack of methods to characterize nonlinear relationships and preserve geometric information in multi-view data.
method Neural network-based approach to infer disentangled, interpretable representations of shared and private latent variables.
result SPLICE yields more interpretable representations by preserving geometry and is more robust to incorrect latent dimensionality.
Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approachcs.CY
Mathematical approach assesses human resource competences accurately.
problem Accurate assessment and representation of human resource competences.
method Detailed quantification scheme and mathematical approach.
result Flexible tools for optimal job assignment and recruitment.
A new method matches similar regions in non-rigid shapes using spectra of differential operators.
problem Evaluating similarity of non-rigid shapes with partiality.
method Alignment of spectra of differential operators (SI-LBO and regular LBO) on a manifold with multiple metrics.
result Matching spectra outperforms competing methods on standard benchmarks.
Paper improves competence estimation of machine learning models.
problem Estimating machine learning performance in real-world scenarios.
method ALICE Score: a pointwise competence estimator considering distributional, data, and model uncertainty.
result Significant improvements in competence prediction over state-of-the-art methods.
Over the past few years, the futures market has been successfully developing in the North-West region. Futures markets are one of the most effective and liquid-visible trading mechanisms. A large number of buyers are forced to compete with each other and raise their prices. A large number of sellers make them reduce pr…
Proposes dynamic model type recommendation for OLP technique.
problem Limited local competence of base-classifiers in uneven data distributions.
method Builds a multi-label meta-classifier to recommend model types based on local data complexity.
result Statistically similar performance to original OLP with fixed base-classifier model.
Data science predicts user interest for midwifery content.
problem Improving midwives' learning and preventing maternal and newborn deaths.
method Forecasting methods using user-generated logs from online learning apps.
result Determining future user interest in midwifery content types.
PQMass assesses generative model quality using chi-squared tests.
problem Assessing the quality of generative models without density assumptions.
method Divides sample space into regions, applies chi-squared tests to p-values.
result Effectively assesses generative model quality, novelty, and diversity.
Bayesian deep learning improves accuracy and calibration without sacrificing scalability.
problem Bayesian inference's potential for deep neural networks.
method Marginalization over optimization, using neural networks' inherent structure and inductive biases.
result Improvements in accuracy and calibration compared to standard training methods.
Comprisk simplifies competing-risks analysis in Python.
problem Analyzing medical time-to-event data with competing risks.
method A scikit-learn-compatible toolkit for competing-risks survival analysis.
result Comprisk provides a unified API for various competing-risks methods.
SurvivalBoost improves prediction of event times in competing risks scenarios.
problem Predicting event times in scenarios with multiple possible outcomes.
method Developed a strictly proper censoring-adjusted scoring rule for stochastic optimization of competing risks.
result SurvivalBoost outperforms 12 state-of-the-art models across various metrics.
Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiabil…
This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small number of mobile robot sensors. We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian…
NCC is inefficient in higher dimensions, NCDA improves performance.
problem Inefficiency of NCC in higher dimensions.
method Combining NCC with LDA to create NCDA.
result NCDA outperforms NCC and competes with LDA and QDA.
Competency questions help experts select best clustering for energy data.
problem Ad hoc and subjective selection of clustering structures by domain experts.
method Formalize expert knowledge and requirements with competency questions.
result Competency questions improve reproducibility and evaluation of clustering applications.
This paper uses neural networks to accurately model competing risks in survival analysis.
problem Ignoring competing risks leads to biased survival estimation in machine learning models.
method The paper introduces constrained monotonic neural networks to model each competing survival distribution.
result The method ensures exact likelihood maximization with reduced computational cost.
Study examines HTE estimation from time-to-event data with competing events.
problem Estimating HTEs from time-to-event data with competing events.
method Outcome modeling approach using plug-in estimators for potential outcomes.
result Competing events introduce new challenges for HTE estimation.
META-DES.H selects competent classifiers using meta-learning and dynamic weighting.
problem Selecting competent classifiers in dynamic ensemble selection.
method META-DES framework using meta-learning and dynamic weighting.
result Improvements in recognition accuracy on 30 datasets.
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risksstat.ML
HACSurv models dependencies between competing risks and censoring for improved survival analysis.
problem Inaccurate survival predictions due to ignoring dependencies between competing risks and censoring.
method HACSurv uses hierarchical Archimedean copulas to model dependencies and cause-specific survival functions.
result HACSurv improves accuracy in survival predictions and captures complex risk interactions.
PyDTS analyzes survival data with discrete intervals and competing risks.
problem Discrete-time survival analysis with competing risks and optional penalization.
method Regularized estimation methods, model evaluation metrics, variable screening tools, and simulation module.
result Supports research and development in discrete-time survival analysis.
New approach generates optimal disturbances for controller verification.
problem Optimizing disturbances for controller verification with blackbox access.
method Online learning approach that adaptively generates disturbances based on controller inputs.
result New algorithm (MOTR) outperforms existing methods in simulated examples.
Article models firm interactions on market with taxation.
problem Analyzing firm interactions on market with taxation.
method Differential equations and Lotka-Volterra model applied to non-competing and competing firms.
result Algorithm for solving competing firms' interaction in taxation.
New method for discrete-time survival analysis with competing risks.
problem Discrete failure time data in survival analysis.
method Proposes a new estimation procedure for discrete-time survival analysis with competing events.
result Offers advantages over existing procedures and integrates regularized regression methods.
Bayesian model predicts evolving guest origin markets in tourism.
problem Forecasting the changing composition of guest origin markets in tourism.
method Developed and applied Bayesian Dirichlet autoregressive moving average (BDARMA) models to Airbnb booking data.
result BDARMA models achieve lower forecast error and competitive performance in guest origin market shares.
LDR models survival with competing risks using nonparametric Bayesian approach.
problem Survival analysis with competing risks and non-monotonic covariate effects.
method Lomax delegate racing, data augmentation, Gibbs sampler, stochastic gradient descent.
result Distinguished performance in survival analysis with competing risks.
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference between analytic embeddings of the joint distribution and the product of the marginals, evaluated at a finite set of locations (features). These features are chose…
Bayesian models predict evolving guest origin markets in tourism.
problem Forecasting the changing composition of guest origin markets in tourism.
method Developed and applied Bayesian Dirichlet autoregressive moving average (BDARMA) models to Airbnb booking data.
result BDARMA models outperform standard benchmarks in forecasting guest origin market shares.
The paper estimates personalized treatment effects in medical settings with competing risks.
problem Estimating treatment effectiveness for specific events in the presence of alternative event types.
method Meta-learners combining Cox regression or random survival forests for risk modeling and elastic net regression or random forests for direct CATE modeling.
result Compared meta-learners in multiple simulation settings, providing practical guidance for model selection.